#!/usr/bin/env python
# -*- encoding: utf-8 -*-
import sys, datetime, json, logging, os
import pandas as pd
import numpy as np
from scipy import fftpack
from xpy3lib.XRetryableQuery import XRetryableQuery
from xpy3lib.utils import db_utils as util
from sicost.config import app_config

def main(tag_tmp, motor_tmp):

    tag = tag_tmp
    motor = motor_tmp
    # motor = 980
    motor = int(motor)
    baseFrequency = motor / 60
    picType = 'all'
    topMinF = 0
    topMaxF = 1000000000000
    topCount = 5
    topCountF = int(topCount)
    frequencyTop = '1,2,3,4,5'
    if frequencyTop != '':
        frequencyTopF = '"' + frequencyTop + '"'
        frequencyTopF = frequencyTopF.replace(',', '\",\"')
        frequencyTopF = eval(frequencyTopF)
        listF = list(frequencyTopF)
    frequencyTopStep = 2
    frequencyTopStepF = int(frequencyTopStep)

    signal_info_hz = 2560
    signal_info_output = 8192
    signal_value = 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    rec_create_time = '2023-12-05 02:06:54.495'
    # signal_info_hz = df_1.loc[0, 'signal_info_hz']
    # signal_info_output = df_1.loc[0, 'signal_info_output']
    # signal_value = df_1.loc[0, 'signal_value']
    # rec_create_time = df_1.loc[0, 'rec_create_time']

    content = dict()

    df_time = getcoordinate(p_picType='time',
                            p_signal_info_hz=signal_info_hz, p_signal_info_output=signal_info_output,
                            p_signal_value=signal_value)
    df_frequency = getcoordinate(p_picType='frequency',
                                 p_signal_info_hz=signal_info_hz, p_signal_info_output=signal_info_output,
                                 p_signal_value=signal_value)
    df_envelope = getcoordinate(p_picType='envelope',
                                p_signal_info_hz=signal_info_hz, p_signal_info_output=signal_info_output,
                                p_signal_value=signal_value)

    mydata_time = df_time.to_json(orient='records', force_ascii=False)
    mydata_time = eval(mydata_time)
    data_time = mydata_time
    content['data_time'] = data_time

    dfF = df_frequency
    ###################################################################################
    dfF = dfF.reset_index(drop=True)
    mydata_frequency = dfF.to_json(orient='records', force_ascii=False)
    mydata_frequency = eval(mydata_frequency)
    data_frequency = mydata_frequency
    content['data_frequency'] = data_frequency

    # print(df_envelope.to_json(orient='records', force_ascii=False))
    mydata_envelope = df_envelope.to_json(orient='records', force_ascii=False)
    # print(mydata_envelope)
    # print('the type of mydata_envelope is %s' % (type(mydata_envelope)))
    mydata_envelope = eval(mydata_envelope)
    # print('the type of mydata_envelope is %s' % (type(mydata_envelope)))
    # print(df_envelope)
    # print('success')
    data_envelope = mydata_envelope
    content['data_envelope'] = data_envelope

    top = list()
    frequencyTop = list()
    df = df_frequency
    writer = pd.ExcelWriter('频域图数据.xlsx')
    df.to_excel(writer, sheet_name='Sheet1', index=False)
    writer.save()
    #####################现在如果没有转速的话，就直接把时域、频域、包络的原始数据返回来，不需要计算，1-5倍频的这个倍频的峰值了


    df_top = df
    df_top = df_top.sort_values(['axisY'], ascending=False)
    df_top = df_top.reset_index(drop=True)
    n = topCountF
    df_top_n = df_top.iloc[:n]
    # print(df_top_n)
    top_data = df_top_n.to_json(orient='records', force_ascii=False)
    top_data = eval(top_data)
    top = top_data


    for i in range(len(listF)):
        # print(listF[i])
        str_tmp = listF[i]
        # s = str[:-1]
        # s = float(s)
        s = float(str_tmp)

        # print(s)
        # sb = s * baseFrequency
        # sb = s * int(motor / 60)
        sb = int(s * (motor / 60))
        df_sb1 = df[(df['axisX'] >= sb)]
        df_sb1 = df_sb1.sort_values(['axisX'], ascending=True)
        df_sb1 = df_sb1.reset_index(drop=True)
        m = frequencyTopStepF + 1
        df_sb1_m = df_sb1.iloc[:m]
        df_sb2 = df[(df['axisX'] <= sb)]
        df_sb2 = df_sb2.sort_values(['axisX'], ascending=False)
        df_sb2 = df_sb2.reset_index(drop=True)
        df_sb2_m = df_sb2.iloc[:m]
        # df_sb0 = df[(df['axisX'] == sb)]
        df_sb = pd.concat([df_sb1_m, df_sb2_m], ignore_index=True)
        df_sb = df_sb.sort_values(['axisY'], ascending=False)
        df_sb = df_sb.reset_index(drop=True)
        # l = 1
        # df_sb_1 = df_sb1.iloc[:l]
        frequencyTop.append({'axisX': df_sb.loc[0, 'axisX'],
                             'axisY': df_sb.loc[0, 'axisY']})
    response_dict = dict()
    response_dict['rec_create_time'] = rec_create_time
    response_dict['top'] = top
    response_dict['frequencyTop'] = frequencyTop
    response_dict['content'] = content
    response_dict['motor'] = motor
    # json.dumps(response_dict)

    # path_name = '/apps/reqms/p_project/get_picture/' + str(tag) + '.json'
    path_name = str(tag) + '.json'
    if os.path.exists(path_name):
        os.remove(path_name)
    new_output1 = json.dumps(response_dict, ensure_ascii=False)
    file = open(path_name, 'a', encoding='utf-8')
    file.write(new_output1)
    file.write('\n')
    file.close()
    return 0

def getcoordinate(p_picType, p_signal_info_hz, p_signal_info_output, p_signal_value):
    p_signal_value = eval(p_signal_value)
    x = [float(v) for v in p_signal_value]
    # print("共有%d点数据" % (len(x)))
    sampling_rate = p_signal_info_hz  # 取样频率(来自传感器说明书)
    fft_size = p_signal_info_output  # FFT处理的数据样本数
    xt = np.asarray(x)
    xt = xt - np.mean(xt)  # 去直流分量
    # p_picType = 'time'
    if p_picType == 'time':
        tstemp = np.linspace(0, int(1e6 / sampling_rate * fft_size), fft_size) / 1e3

        arr3 = np.vstack((tstemp, xt))
        arr3t = arr3.T
        df = pd.DataFrame(arr3t, columns=['axisX', 'axisY'])
    if p_picType == 'frequency':
        am = np.fft.fft(xt)  # 对希尔伯特变换后的at做fft变换获得幅值
        am = np.abs(am)  # 对幅值求绝对值（此时的绝对值很大）
        am = am / len(am) * 2
        # am = am / p_signal_info_output * 2
        am = am[0: int(len(am) / 2)]
        freq = np.fft.fftfreq(len(xt), d=1 / sampling_rate)  # 获取fft频率，此时包括正频率和负频率
        freq = freq[0:int(len(freq) / 2)]  # 获取正频率

        arr3 = np.vstack((freq, am))
        arr3t = arr3.T
        df = pd.DataFrame(arr3t, columns=['axisX', 'axisY'])

    if p_picType == 'envelope':
        # xt = np.asarray(x)
        ht = fftpack.hilbert(xt)
        # print(ht)
        at = np.sqrt(ht ** 2 + xt ** 2)  # at = sqrt(xt^2 + ht^2)
        sampling_rate = sampling_rate
        at = at - np.mean(at)  # 去直流分量
        am = np.fft.fft(at)  # 对希尔伯特变换后的at做fft变换获得幅值
        am = np.abs(am)  # 对幅值求绝对值（此时的绝对值很大）
        am = am / len(am) * 2
        am = am[0: int(len(am) / 2)]
        freq = np.fft.fftfreq(len(at), d=1 / sampling_rate)  # 获取fft频率，此时包括正频率和负频率
        freq = freq[0:int(len(freq) / 2)]  # 获取正频率

        arr3 = np.vstack((freq, am))
        arr3t = arr3.T
        df = pd.DataFrame(arr3t, columns=['axisX', 'axisY'])
    return df
if __name__ == '__main__':
    start = datetime.datetime.now()



    tag_tmp = 'BS_320229E13_MBSIDERBG_AXVIB_8K_SPEED_WAVE_1000'
    motor_tmp = 920



    status = main(tag_tmp, motor_tmp)

    # for tag_tmp in tag_list:
    #     status = main(tag_tmp)
    elapsed = float((datetime.datetime.now() - start).seconds)
    print("Time Used 4 All ----->>>> %f seconds" % (elapsed))
    sys.exit(0)